Search

Agent‐based model (ABM) has been widely used to study infectious disease transmission by simulating behaviors and interactions of autonomous individuals called agents. In the ABM, agent states, for example infected or susceptible, are assigned according to a set of simple rules, and a complex dynamics of disease transmission is described by the collective states of agents over time. Despite the flexibility in real‐world modeling, ABMs have received less attention by statisticians because of the intractable likelihood functions which lead to difficulty in estimating parameters and quantifying uncertainty around model outputs. To overcome this limitation, a Bayesian framework that treats the entire ABM as a Hidden Markov Model has been previously proposed. However, existing approach is limited due to computational inefficiency and unidentifiability of parameters. We extend the ABM approach within Bayesian framework to study infectious disease transmission addressing these limitations. We estimate the hidden states, represented by individual agent's states over time, and the model parameters by applying an improved particle Markov Chain Monte Carlo algorithm, that accounts for computing efficiency. We further evaluate the performance of the approach for parameter recovery and prediction, along with sensitivity to prior assumptions under various simulation conditions. Finally, we apply the proposed approach to the study of COVID‐19 outbreak on Diamond Princess cruise ship. We examine the differences in transmission by key demographic characteristics, while considering two different networks and limited COVID‐19 testing in the cruise. [ABSTRACT FROM AUTHOR]

Background: Internalizing and externalizing problems account for over 75% of the mental health burden in children and adolescents in the US, with higher burden among minority children. While complex interactions of multilevel factors are associated with these outcomes and may enable early identification of children in higher risk, prior research has been limited by data and application of traditional analysis methods. In this case example focused on Asian American children, we address the gap by applying data-driven statistical and machine learning methods to study clusters of mental health trajectories among children, investigate optimal predictions of children at high-risk cluster, and identify key early predictors. Methods: Data from the US Early Childhood Longitudinal Study 2010–2011 were used. Multilevel information provided by children, families, teachers, schools, and care-providers were considered as predictors. Unsupervised machine learning algorithm was applied to identify groups of internalizing and externalizing problems trajectories. For prediction of high-risk group, ensemble algorithm, Superlearner, was implemented by combining several supervised machine learning algorithms. Performance of Superlearner and candidate algorithms, including logistic regression, was assessed using discrimination and calibration metrics via crossvalidation. Variable importance measures along with partial dependence plots were utilized to rank and visualize key predictors. Findings: We found two clusters suggesting high- and low-risk groups for both externalizing and internalizing problems trajectories. While Superlearner had overall best discrimination performance, logistic regression had comparable performance for externalizing problems but worse for internalizing problems. Predictions from logistic regression were not well calibrated compared to those from Superlearner, however they were still better than few candidate algorithms. Important predictors identified were combination of test scores, child factors, teacher rated scores, and contextual factors, which showed non-linear associations with predicted probabilities. Conclusions: We demonstrated the application of data-driven analytical approach to predict mental health outcomes among Asian American children. Findings from the cluster analysis can inform critical age for early intervention, while prediction analysis has potential to inform intervention programing prioritization decisions. However, to better understand external validity, replicability, and value of machine learning in broader mental health research, more studies applying similar analytical approach is needed. [ABSTRACT FROM AUTHOR]

Background: While emerging studies suggest that the COVID-19 pandemic caused disruptions in routine healthcare utilization, the full impact of the pandemic on healthcare utilization among diverse group of patients with type 2 diabetes is unclear. The purpose of this study is to examine trends in healthcare utilization, including in-person and telehealth visits, among U.S. veterans with type 2 diabetes before, during and after the onset of the COVID-19 pandemic, by demographics, pre-pandemic glycemic control, and geographic region. Methods: We longitudinally examined healthcare utilization in a large national cohort of veterans with new diabetes diagnoses between January 1, 2008 and December 31, 2018. The analytic sample was 733,006 veterans with recently-diagnosed diabetes, at least 1 encounter with veterans administration between March 2018–2020, and followed through March 2021. Monthly rates of glycohemoglobin (HbA1c) measurements, in-person and telehealth outpatient visits, and prescription fills for diabetes and hypertension medications were compared before and after March 2020 using interrupted time-series design. Log-linear regression model was used for statistical analysis. Secular trends were modeled with penalized cubic splines. Results: In the initial 3 months after the pandemic onset, we observed large reductions in monthly rates of HbA1c measurements, from 130 (95%CI,110–140) to 50 (95%CI,30–80) per 1000 veterans, and in-person outpatient visits, from 1830 (95%CI,1640–2040) to 810 (95%CI,710–930) per 1000 veterans. However, monthly rates of telehealth visits doubled between March 2020–2021 from 330 (95%CI,310–350) to 770 (95%CI,720–820) per 1000 veterans. This pattern of increases in telehealth utilization varied by community type, with lowest increase in rural areas, and by race/ethnicity, with highest increase among non-hispanic Black veterans. Combined in-person and telehealth outpatient visits rebounded to pre-pandemic levels after 3 months. Despite notable changes in HbA1c measurements and visits during that initial window, we observed no changes in prescription fills rates. Conclusions: Healthcare utilization among veterans with diabetes was substantially disrupted at the onset of the pandemic, but rebounded after 3 months. There was disparity in uptake of telehealth visits by geography and race/ethnicity. [ABSTRACT FROM AUTHOR]

There is some, but inconsistent, evidence to suggest that matching patient treatment preference enhances treatment engagement and outcome. The current study examined differential preferences and factors associated with treatment preference for 12-week group cognitive behavioral therapy (CBT), yoga, or stress education in 226 adults with generalized anxiety disorder (GAD; 70% female, Mean age = 33 ± 13.5). In a subsample of 165 patients who reported an intervention preference and were randomized to yoga or CBT, we further examined whether match to preferred intervention improved the primary treatment outcome (responder status on Clinical Global Impressions Scale) and engagement (dropout, homework compliance). Preferences for CBT (44%) and yoga (40%) were similar among patients. Women tended to prefer yoga (OR = 2.75, p =.01) and CBT preference was associated with higher baseline perceived stress (OR = 0.92, p =.04) and self-consciousness meta-cognitions (OR = 0.90, p =.02). Among those not matched to their preference, treatment response was higher for those receiving CBT than yoga (OR = 11.73, p =.013); there were no group differences for those matched to their treatment preference. In yoga, those who received their preference were more likely to drop than those who did not (OR = 3.02, 95% CI = [1.20, 7.58], p =.037). This was not the case for CBT (OR = 0.37, 95% CI = [0.13, 1.03], p =.076). Preference match did not predict homework compliance. Overall, results suggest that treatment preference may be important to consider to optimize outcome and engagement; however, it may vary by treatment modality. Future research incorporating preference, especially with yoga for anxiety, is aligned with personalized medicine. clinicaltrials.gov: NCT01912287 ; https://clinicaltrials.gov/ct2/show/NCT01912287. • Proportion of patients with GAD who preferred CBT or yoga was similar. • Receiving the preferred treatment vs. not did not alter outcomes. • For those who did not receive their preference, CBT had better outcomes than yoga. • For yoga, preference match had higher dropout than non-match. [ABSTRACT FROM AUTHOR]

Objective: We examined whether relative availability of fast-food restaurants and supermarkets mediates the association between worse neighborhood socioeconomic conditions and risk of developing type 2 diabetes (T2D).Research Design and Methods: As part of the Diabetes Location, Environmental Attributes, and Disparities Network, three academic institutions used harmonized environmental data sources and analytic methods in three distinct study samples: 1) the Veterans Administration Diabetes Risk (VADR) cohort, a national administrative cohort of 4.1 million diabetes-free veterans developed using electronic health records (EHRs); 2) Reasons for Geographic and Racial Differences in Stroke (REGARDS), a longitudinal, epidemiologic cohort with Stroke Belt region oversampling (N = 11,208); and 3) Geisinger/Johns Hopkins University (G/JHU), an EHR-based, nested case-control study of 15,888 patients with new-onset T2D and of matched control participants in Pennsylvania. A census tract-level measure of neighborhood socioeconomic environment (NSEE) was developed as a community type-specific z-score sum. Baseline food-environment mediators included percentages of 1) fast-food restaurants and 2) food retail establishments that are supermarkets. Natural direct and indirect mediating effects were modeled; results were stratified across four community types: higher-density urban, lower-density urban, suburban/small town, and rural.Results: Across studies, worse NSEE was associated with higher T2D risk. In VADR, relative availability of fast-food restaurants and supermarkets was positively and negatively associated with T2D, respectively, whereas associations in REGARDS and G/JHU geographies were mixed. Mediation results suggested that little to none of the NSEE-diabetes associations were mediated through food-environment pathways.Conclusions: Worse neighborhood socioeconomic conditions were associated with higher T2D risk, yet associations are likely not mediated through food-environment pathways. [ABSTRACT FROM AUTHOR]

Machine learning algorithms are increasingly used in the clinical literature, claiming advantages over logistic regression. However, they are generally designed to maximize the area under the receiver operating characteristic curve. While area under the receiver operating characteristic curve and other measures of accuracy are commonly reported for evaluating binary prediction problems, these metrics can be misleading. We aim to give clinical and machine learning researchers a realistic medical example of the dangers of relying on a single measure of discriminatory performance to evaluate binary prediction questions. Prediction of medical complications after surgery is a frequent but challenging task because many post-surgery outcomes are rare. We predicted post-surgery mortality among patients in a clinical registry who received at least one aortic valve replacement. Estimation incorporated multiple evaluation metrics and algorithms typically regarded as performing well with rare outcomes, as well as an ensemble and a new extension of the lasso for multiple unordered treatments. Results demonstrated high accuracy for all algorithms with moderate measures of cross-validated area under the receiver operating characteristic curve. False positive rates were < 1%, however, true positive rates were < 7%, even when paired with a 100% positive predictive value, and graphical representations of calibration were poor. Similar results were seen in simulations, with the addition of high area under the receiver operating characteristic curve (> 90%) accompanying low true positive rates. Clinical studies should not primarily report only area under the receiver operating characteristic curve or accuracy. [ABSTRACT FROM AUTHOR]

Background We examined effects of single-nucleotide variants (SNVs) of IL1RN , the gene encoding the anti-inflammatory interleukin 1 receptor antagonist (IL-1Ra), on the cytokine release syndrome (CRS) and mortality in patients with acute severe respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Methods IL1RN CTA haplotypes formed from 3 SNVs (rs419598, rs315952, rs9005) and the individual SNVs were assessed for association with laboratory markers of inflammation and mortality. We studied 2589 patients hospitalized with SARS-CoV-2 between March 2020 and March 2021. Results Mortality was 15.3% and lower in women than men (13.1% vs 17.3%, P =.0003). Carriers of the CTA-1/2 IL1RN haplotypes exhibited decreased inflammatory markers and increased plasma IL-1Ra. Evaluation of the individual SNVs of the IL1RN, carriers of the rs419598 C/C SNV exhibited significantly reduced inflammatory biomarker levels and numerically lower mortality compared to the C/T-T/T genotype (10.0% vs 17.8%, P =.052) in men, with the most pronounced association observed in male patients ≤74 years old, whose mortality was reduced by 80% (3.1% vs 14.0%, P =.030). Conclusions The IL1RN haplotype CTA and C/C variant of rs419598 are associated with attenuation of the CRS and decreased mortality in men with acute SARS-CoV-2 infection. The data suggest that the IL1RN pathway modulates the severity of coronavirus disease 2019 (COVID-19) via endogenous anti-inflammatory mechanisms. [ABSTRACT FROM AUTHOR]

Percutaneous coronary interventions (PCIs) are nonsurgical procedures to open blocked blood vessels to the heart, frequently using a catheter to place a stent. The catheter can be inserted into the blood vessels using an artery in the groin or an artery in the wrist. Because clinical trials have indicated that access via the wrist may result in fewer post procedure complications, shortening the length of stay, and ultimately cost less than groin access, adoption of access via the wrist has been encouraged. However, patients treated in usual care are likely to differ from those participating in clinical trials, and there is reason to believe that the effectiveness of wrist access may differ between males and females. Moreover, the choice of artery access strategy is likely to be influenced by patient or physician unmeasured factors. To study the effectiveness of the two artery access site strategies on hospitalization charges, we use data from a state-mandated clinical registry including 7963 patients undergoing PCI. A hierarchical Bayesian likelihood-based instrumental variable analysis under a latent index modeling framework is introduced to jointly model outcomes and treatment status. Our approach accounts for unobserved heterogeneity via a latent factor structure, and permits nonparametric error distributions with Dirichlet process mixture models. Our results demonstrate that artery access in the wrist reduces hospitalization charges compared to access in the groin, with a higher mean reduction for male patients. for this article are available online. [ABSTRACT FROM AUTHOR]

Objective: Scalable, accessible forms of behavioral therapy for migraine prevention are needed. We assessed the feasibility and acceptability of progressive muscle relaxation (PMR) delivered by a smartphone application (app) in the Primary Care setting. Methods: This pilot study was a non‐blinded, randomized, parallel‐arm controlled trial of adults with migraine and 4+ headache days/month. Eligible participants spoke English and owned a smartphone. All participants were given the RELAXaHEAD app which includes an electronic headache diary. Participants were randomized to receive 1 of the 2 versions of the app‐one with PMR and the other without PMR. The primary outcomes were measures of feasibility (adherence to the intervention and diary entries during the 90‐day interval) and acceptability (satisfaction levels). We conducted exploratory analyses to determine whether there was a change in Migraine Disability Assessment Scale (MIDAS) scores or a change in headache days. Results: Of 139 participants (77 PMR, 62 control), 116 (83%) were female, mean age was 41.7 ± 12.8 years. Most patients 108/139 (78%) had moderate‐severe disability. Using a 1‐5 Likert scale, participants found the app easy to use (mean 4.2 ± 0.7) and stated that they would be happy to engage in the PMR intervention again (mean 4.3 ± 0.6). For the first 6 weeks, participants practiced PMR 2‐4 days/week. Mean per session duration was 11.1 ± 8.3 minutes. Relative to the diary‐only group, the PMR group showed a greater non‐significant decline in mean MIDAS scores (−8.7 vs −22.7, P =.100) corresponding to a small‐moderate mean effect size (Cohen's d = 0.38). Conclusion: Smartphone‐delivered PMR may be an acceptable, accessible form of therapy for migraine. Mean effects show a small‐moderate mean effect size in disability scores. [ABSTRACT FROM AUTHOR]

Background: There is concern about the potential of an increased risk related to medications that act on the renin-angiotensin-aldosterone system in patients exposed to coronavirus disease 2019 (Covid-19), because the viral receptor is angiotensin-converting enzyme 2 (ACE2).Methods: We assessed the relation between previous treatment with ACE inhibitors, angiotensin-receptor blockers, beta-blockers, calcium-channel blockers, or thiazide diuretics and the likelihood of a positive or negative result on Covid-19 testing as well as the likelihood of severe illness (defined as intensive care, mechanical ventilation, or death) among patients who tested positive. Using Bayesian methods, we compared outcomes in patients who had been treated with these medications and in untreated patients, overall and in those with hypertension, after propensity-score matching for receipt of each medication class. A difference of at least 10 percentage points was prespecified as a substantial difference.Results: Among 12,594 patients who were tested for Covid-19, a total of 5894 (46.8%) were positive; 1002 of these patients (17.0%) had severe illness. A history of hypertension was present in 4357 patients (34.6%), among whom 2573 (59.1%) had a positive test; 634 of these patients (24.6%) had severe illness. There was no association between any single medication class and an increased likelihood of a positive test. None of the medications examined was associated with a substantial increase in the risk of severe illness among patients who tested positive.Conclusions: We found no substantial increase in the likelihood of a positive test for Covid-19 or in the risk of severe Covid-19 among patients who tested positive in association with five common classes of antihypertensive medications. [ABSTRACT FROM AUTHOR]

We study regularized estimation in high‐dimensional longitudinal classification problems, using the lasso and fused lasso regularizers. The constructed coefficient estimates are piecewise constant across the time dimension in the longitudinal problem, with adaptively selected change points (break points). We present an efficient algorithm for computing such estimates, based on proximal gradient descent. We apply our proposed technique to a longitudinal data set on Alzheimer's disease from the Cardiovascular Health Study Cognition Study. Using data analysis and a simulation study, we motivate and demonstrate several practical considerations such as the selection of tuning parameters and the assessment of model stability. While race, gender, vascular and heart disease, lack of caregivers, and deterioration of learning and memory are all important predictors of dementia, we also find that these risk factors become more relevant in the later stages of life. [ABSTRACT FROM AUTHOR]

The article reflects the views of author which focus on the positive association observed in our study between the use of oral anticoagulants and the risk of Covid-19. Topics include use of antihypertensive therapy is not relevant to the outcome in patients with Covid-19; and duration of hypertension or duration of treatment with specific agents will be accurately reflected in the electronic health records.

Background: Environmental exposures account for a growing proportion of global mortality. Large cohort studies are needed to characterize the independent impact of environmental exposures on mortality in low-income settings. Methods: We collected data on individual and environmental risk factors for a multiethnic cohort of 50,045 individuals in a low-income region in Iran. Environmental risk factors included: ambient fine particular matter air pollution; household fuel use and ventilation; proximity to traffic; distance to percutaneous coronary intervention (PCI) center; socioeconomic environment; population density; local land use; and nighttime light exposure. We developed a spatial survival model to estimate the independent associations between these environmental exposures and all-cause and cardiovascular mortality. Findings: Several environmental factors demonstrated associations with mortality after adjusting for individual risk factors. Ambient fine particulate matter air pollution predicted all-cause mortality (per μg/m3, HR 1.20, 95% CI 1.07, 1.36) and cardiovascular mortality (HR 1.17, 95% CI 0.98, 1.39). Biomass fuel use without chimney predicted all-cause mortality (reference = gas, HR 1.23, 95% CI 0.99, 1.53) and cardiovascular mortality (HR 1.36, 95% CI 0.99, 1.87). Kerosene fuel use without chimney predicted all-cause mortality (reference = gas, HR 1.09, 95% CI 0.97, 1.23) and cardiovascular mortality (HR 1.19, 95% CI 1.01, 1.41). Distance to PCI center predicted all-cause mortality (per 10km, HR 1.01, 95% CI 1.004, 1.022) and cardiovascular mortality (HR 1.02, 95% CI 1.004, 1.031). Additionally, proximity to traffic predicted all-cause mortality (HR 1.13, 95% CI 1.01, 1.27). In a separate validation cohort, the multivariable model effectively predicted both all-cause mortality (AUC 0.76) and cardiovascular mortality (AUC 0.81). Population attributable fractions demonstrated a high mortality burden attributable to environmental exposures. Interpretation: Several environmental factors predicted cardiovascular and all-cause mortality, independent of each other and of individual risk factors. Mortality attributable to environmental factors represents a critical opportunity for targeted policies and programs. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions There is growing recognition that healthcare should align with individuals' health priorities; however, these priorities remain undefined, especially among older adults. The Rehabilitation Using Mobile Health for Older Adults with Ischemic Heart Disease in the Home Setting (RESILIENT) trial, designed to test the efficacy of mobile health cardiac rehabilitation (mHealth‐CR) in an older cohort, also measures the attainment of participant‐defined health outcome goals as a prespecified secondary endpoint. This study aimed to characterize the health priorities of older adults with ischemic heart disease (IHD) using goal attainment scaling—a technique for measuring individualized goal achievement—in a sample of 100 RESILIENT participants.The ongoing RESILIENT trial randomizes patients aged ≥65 years with IHD (defined as hospitalization for acute coronary syndrome and/or coronary revascularization), to receive mHealth‐CR or usual care. For the current study, we qualitatively coded baseline goal attainment scales from randomly selected batches of 20 participants to identify participants' cardiac rehabilitation outcome goals and their perceptions of barriers and action plans for goal attainment. We used a deductive framework (i.e., 4 value categories from Patient Priorities Care) and inductive approaches to code and analyze interviews until thematic saturation.This sample of 100 older adults set diverse health outcome goals. Most (54.6%) prioritized physical activity, fewer (17.1%) identified symptom management, fewer still (13.7%) prioritized health metrics, mostly comprised of weight loss goals (10.3%), and the fewest (<4%) were related to clinical metrics such as reducing cholesterol or preventing hospital readmission. Participants anticipated extrinsic (access to places to exercise, time) and intrinsic (non‐cardiac pain, motivation) barriers. Action plans detailed strategies for exercise, motivation, accountability, and overcoming time constraints.Using goal attainment scaling, we elicited specific and measurable goals among older adults with IHD beginning cardiac rehabilitation. Priorities were predominantly functional, diverging from clinical metrics emphasized by clinicians and healthcare systems. [ABSTRACT FROM AUTHOR]

We study regularized estimation in high-dimensional longitudinal classification problems, using the lasso and fused lasso regularizers. The constructed coefficient estimates are piecewise constant across the time dimension in the longitudinal problem, with adaptively selected change points (break points). We present an efficient algorithm for computing such estimates, based on proximal gradient descent. We apply our proposed technique to a longitudinal data set on Alzheimer's disease from the Cardiovascular Health Study Cognition Study. Using data analysis and a simulation study, we motivate and demonstrate several practical considerations such as the selection of tuning parameters and the assessment of model stability. While race, gender, vascular and heart disease, lack of caregivers, and deterioration of learning and memory are all important predictors of dementia, we also find that these risk factors become more relevant in the later stages of life. [ABSTRACT FROM AUTHOR]

To determine whether a smartphone application (app) with an electronic headache diary and a progressive muscle relaxation (PMR) intervention is feasible and acceptable to people presenting to the Emergency Department (ED) with migraine. This single arm prospective study assessed feasibility by actual use of the app and acceptability by satisfaction with the app. We report preliminary data on change in migraine disability and headache days. The 51 participants completed PMR sessions on a mean of 13 ± 19 (0,82) days for the 90-day study period, lasting a median of 11 min (IQR 6.5, 17) each. Median number of days of diary use was 34 (IQR 10, 77). Diaries were completed at least twice a week in half of study weeks (337/663). Participants were likely (≥4/5 on a 5-point Likert scale) to recommend both the app (85%) and PMR (91%). MIDAS scores significantly decreased by a mean of 38 points/participant (p < 0.0001). More frequent PMR use was associated with a higher odds of headache free days (p = 0.0148). Smartphone-based PMR introduced to patients who present to the ED for migraine is feasible and acceptable. More frequent users have more headache free days. Future work should focus on intervention engagement. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Older adults with inflammatory bowel disease (IBD) are at higher risk for postoperative complications as compared to their younger counterparts; however, factors contributing to this are unknown. We assessed risk factors associated with adverse IBD-related surgical outcomes, evaluated trends in emergency surgery, and explored differential risks by age. METHODS: Using the American College of Surgeons National Surgical Quality Improvement Program database, we identified adults ≥18 years of age who underwent an IBD-related intestinal resection from 2005 to 2019. Our primary outcome included a 30-day composite of mortality, readmission, reoperation, and/or major postoperative complication. RESULTS: Overall, 49,746 intestinal resections were performed with 9,390 (18.8%) occurring among older adults with IBD. Nearly 37% of older adults experienced an adverse outcome as compared to 28.1% among younger adults with IBD (P < 0.01). Among all adults with IBD, the presence of preoperative sepsis (adjusted odds ratio [aOR], 2.08; 95% confidence interval [CI] 1.94-2.24), malnutrition (aOR, 1.22; 95% CI 1.14-1.31), dependent functional status (aOR, 6.92; 95% CI 4.36-11.57), and requiring emergency surgery (aOR, 1.50; 95% CI 1.38-1.64) increased the odds of an adverse postoperative outcome, with similar results observed when stratifying by age. Furthermore, 8.8% of surgeries among older adults were emergent, with no change observed over time (P 5 0.16). DISCUSSION: Preoperative factors contributing to the risk of an adverse surgical outcome are similar between younger and older individuals with IBD, and include elements such as malnutrition and functional status. Incorporating these measures into surgical decision-making can reduce surgical delays in older individuals at low risk and help target interventions in those at high risk, transforming care for thousands of older adults with IBD. [ABSTRACT FROM AUTHOR]

Large-scale longitudinal studies evaluating influences of the built environment on risk for type 2 diabetes (T2D) are scarce, and findings have been inconsistent. To evaluate whether land use environment (LUE), a proxy of neighborhood walkability, is associated with T2D risk across different US community types, and to assess whether the association is modified by food environment. The Veteran's Administration Diabetes Risk (VADR) study is a retrospective cohort of diabetes-free US veteran patients enrolled in VA primary care facilities nationwide from January 1, 2008, to December 31, 2016, and followed longitudinally through December 31, 2018. A total of 4,096,629 patients had baseline addresses available in electronic health records that were geocoded and assigned a census tract-level LUE score. LUE scores were divided into quartiles, where a higher score indicated higher neighborhood walkability levels. New diagnoses for T2D were identified using a published computable phenotype. Adjusted time-to-event analyses using piecewise exponential models were fit within four strata of community types (higher-density urban, lower-density urban, suburban/small town, and rural). We also evaluated effect modification by tract-level food environment measures within each stratum. In adjusted analyses, higher LUE had a protective effect on T2D risk in rural and suburban/small town communities (linear quartile trend test p-value <0.001). However, in lower density urban communities, higher LUE increased T2D risk (linear quartile trend test p-value <0.001) and no association was found in higher density urban communities (linear quartile trend test p-value = 0.317). Particularly strong protective effects were observed for veterans living in suburban/small towns with more supermarkets and more walkable spaces (p-interaction = 0.001). Among veterans, LUE may influence T2D risk, particularly in rural and suburban communities. Food environment may modify the association between LUE and T2D. [ABSTRACT FROM AUTHOR]

Purpose: To examine how the choice of neighborhood food environment definition impacts the association with diet.Methods: Using food frequency questionnaire data from the Reasons for Geographic and Racial Differences in Stroke study at baseline (2003-2007), we calculated participants' dietary inflammation score (DIS) (n = 20,331); higher scores indicate greater pro-inflammatory exposure. We characterized availability of supermarkets and fast food restaurants using several geospatial measures, including density (i.e., counts/km2) and relative measures (i.e., percentage of all food stores or restaurants); and various buffer distances, including administrative units (census tract) and empirically derived buffers ("classic" network, "sausage" network) tailored to community type (higher density urban, lower density urban, suburban/small town, rural). Using generalized estimating equations, we estimated the association between each geospatial measure and DIS, controlling for individual- and neighborhood-level sociodemographics.Results: The choice of buffer-based measure did not change the direction or magnitude of associations with DIS. Effect estimates derived from administrative units were smaller than those derived from tailored empirically derived buffer measures. Substantively, a 10% increase in the percentage of fast food restaurants using a "classic" network buffer was associated with a 6.3 (SE = 1.17) point higher DIS (P< .001). The relationship between the percentage of supermarkets and DIS, however, was null. We observed high correlation coefficients between buffer-based density measures of supermarkets and fast food restaurants (r = 0.73-0.83), which made it difficult to estimate independent associations by food outlet type.Conclusions: Researchers should tailor buffer-based measures to community type in future studies, and carefully consider the theoretical and statistical implications for choosing relative (vs. absolute) measures. [ABSTRACT FROM AUTHOR]

Although hand temperature and electromyograph biofeedback have evidence for migraine prevention, to date, no study has evaluated heartrate variability (HRV) biofeedback for migraine. 2-arm randomized trial comparing an 8-week app-based HRV biofeedback (HeartMath) to waitlist control. Feasibility/acceptability outcomes included number and duration of sessions, satisfaction, barriers and adverse events. Primary clinical outcome was Migraine-Specific Quality of Life Questionnaire (MSQv2). There were 52 participants (26/arm). On average, participants randomized to the Hearthmath group completed 29 sessions (SD = 29, range: 2–86) with an average length of 6:43 min over 36 days (SD = 27, range: 0, 88) before discontinuing. 9/29 reported technology barriers. 43% said that they were likely to recommend Heartmath to others. Average MSQv2 decreases were not significant between the Heartmath and waitlist control (estimate = 0.3, 95% CI = −3.1 - 3.6). High users of Heartmath reported a reduction in MSQv2 at day 30 (−12.3 points, p = 0.010) while low users did not (p = 0.765). App-based HRV biofeedback was feasible and acceptable on a time-limited basis for people with migraine. Changes in the primary clinical outcome did not differ between biofeedback and control; however, high users of the app reported more benefit than low users. [ABSTRACT FROM AUTHOR]

Exposure to particulate matter ≤2.5 μm in diameter (PM 2.5) and ozone (O 3) has been linked to numerous harmful health outcomes. While epidemiologic evidence has suggested a positive association with type 2 diabetes (T2D), there is heterogeneity in findings. We evaluated exposures to PM 2.5 and O 3 across three large samples in the US using a harmonized approach for exposure assignment and covariate adjustment. Data were obtained from the Veterans Administration Diabetes Risk (VADR) cohort (electronic health records [EHRs]), the Reasons for Geographic and Racial Disparities in Stroke (REGARDS) cohort (primary data collection), and the Geisinger health system (EHRs), and reflect the years 2003–2016 (REGARDS) and 2008–2016 (VADR and Geisinger). New onset T2D was ascertained using EHR information on medication orders, laboratory results, and T2D diagnoses (VADR and Geisinger) or report of T2D medication or diagnosis and/or elevated blood glucose levels (REGARDS). Exposure was assigned using pollutant annual averages from the Downscaler model. Models stratified by community type (higher density urban, lower density urban, suburban/small town, or rural census tracts) evaluated likelihood of new onset T2D in each study sample in single- and two-pollutant models of PM 2.5 and O 3. In two pollutant models, associations of PM 2.5 , and new onset T2D were null in the REGARDS cohort except for in suburban/small town community types in models that also adjusted for NSEE, with an odds ratio (95% CI) of 1.51 (1.01, 2.25) per 5 μg/m3 of PM 2.5. Results in the Geisinger sample were null. VADR sample results evidenced nonlinear associations for both pollutants; the shape of the association was dependent on community type. Associations between PM 2.5 , O 3 and new onset T2D differed across three large study samples in the US. None of the results from any of the three study populations found strong and clear positive associations. • PM 2.5 and O 3 were minimally associated with new onset type 2 diabetes (T2D) in three large study samples in the United States. • Results did not substantially differ between single and two pollutant models. • Associations of PM 2.5 and O 3 with new onset T2D were impacted by adjustment for neighborhood socio-economic disadvantage. [ABSTRACT FROM AUTHOR]

Objective: To characterize the hospitalization and death rates among patients with inflammatory arthritis (IA) affected by coronavirus disease 2019 (COVID‐19) and to analyze the associations of comorbidities and immunomodulatory medications with infection outcomes. Methods: Data on clinical and demographic features, maintenance treatment, disease course, and outcomes in individuals with IA (rheumatoid arthritis and spondyloarthritis) with symptomatic COVID‐19 infection were prospectively assessed via web‐based questionnaire followed by individual phone calls and electronic medical record review. Baseline characteristics and medication use were summarized for hospitalized and ambulatory patients, and outcomes with the different medication classes were compared using multivariable logistic regression. Results: A total of 103 patients with IA were included in the study (80 with confirmed COVID‐19 and 23 with high suspicion of COVID‐19). Hospitalization was required in 26% of the participants, and 4% died. Patients who were hospitalized were significantly more likely to be older (P < 0.001) and have comorbid hypertension (P = 0.001) and chronic obstructive pulmonary disease (P = 0.02). IA patients taking oral glucocorticoids had an increased likelihood of being admitted for COVID‐19 (P < 0.001), while those receiving maintenance anticytokine biologic therapies did not. Conclusion: Among patients with underlying IA, COVID‐19 outcomes were worse in those receiving glucocorticoids but not in patients receiving maintenance anticytokine therapy. Further work is needed to understand whether immunomodulatory therapies affect COVID‐19 incidence. [ABSTRACT FROM AUTHOR]

The article explores the understanding of the implications of Covid-19 in patients with immune-mediated inflammatory disease and the effects of anticytokine and other immunosuppressive therapies needed to guide clinicians in the care of patients with psoriasis, rheumatoid arthritis and the psoriatic arthritis. It mentions that hospitalization concerns among patients with immune-mediated inflammatory disease.

Diabetes causes significant morbidity and mortality among adults in the US, yet incidence varies across the country, suggesting that neighborhood factors may contribute to geographic disparities in diabetes. In this longitudinal study, we examined the association between neighborhood food environment and risk of newly diagnosed ("incident") diabetes in different communities (high density urban, low density urban, suburban, and rural). We created a retrospective dynamic, national cohort of 6,082,018 diabetes free US veterans identified from the Veterans Affairs electronic health records, enrolled between 2008 and 2016, and followed through 2018. Incident diabetes was defined as: 2 clinical encounters with diabetes ICD codes, a documented prescription for diabetes medication other than metformin or acarbose alone, or 1 clinical encounter with diabetes ICD codes and 2 elevated A1C (≥6.5%). Five-year average counts of fast-food restaurants and supermarkets relative to all other food outlets were used to generate two census tract-level neighborhood measures. We examined the association between food environment and incident diabetes using piecewise exponential survival models with county-level random effects. The average age of cohort participants was 59 years; the majority were non-Hispanic white (76.3%) and male (92.2%). Relative density of fast-food restaurants was associated with a modest increased risk of diabetes in all community types; adjusted hazard ratios (aHR) in high density urban, low density urban, and suburban communities were each 1.1, in rural communities was 1.2 (p < 0.05 for all). Relative density of supermarkets was associated with lower diabetes risk in suburban and rural communities only (aHR 0.7, 0.8, respectively, p <0.05). Neighborhood fast food and supermarkets influenced diabetes risk among US veterans in multiple community types, suggesting potential avenues for action to address the diabetes burden. Disclosure: R. Kanchi: None. G. Imperatore: None. B. Elbel: None. L. Thorpe: None. P. M. Lopez: None. P. E. Rummo: None. D. C. Lee: None. S. Adhikari: Research Support; Self; Johnson & Johnson. M. D. Schwartz: None. S. Avramovic: None. K. R. Siegel: None. D. B. Rolka: None. Funding: Centers for Disease Control and Prevention [ABSTRACT FROM AUTHOR]